WBL: A data-driven water authority

Like all water authorities, Waterschapsbedrijf Limburg (WBL) currently faces a wealth of challenges. A growing population, strict EU legislation, climate change and the imminent retirement of an experienced workforce are making it increasingly difficult to meet the needs of its public.

As a result, the water authority sought to implement data-driven operations. Its goal was to gain a greater understanding of how each of its assets functions, and improve its ability to analyse past events and anticipate future failures and emergencies. Working with Royal HaskoningDHV, WBL was able to take a first step in realising this ambition.

The challenge

Embracing Data-Driven Working

Waterschapsbedrijf Limburg (WBL) is responsible for providing essential clean water for 500,000 households and 30,000 companies in the province of Limburg, the Netherlands. And they take this role very seriously. That is why they innovate every day to show that things can be done even better, more sustainable and more efficiently.

Turning this vision into a reality is far from easy. WBL knew that to achieve its goal it would need to experiment with the latest technological advancements, including big data, digital twins and AI.

“The future vision of our company is that we will become a data-driven organisation,” says Leon Verhaegen, Senior Project Leader of ICT & Innovation at WBL. “That data-driven organisation wants to know how all assets function, as well as analysing all past events to help understand them and anticipate the future.”

The solution

A future we must embark upon together

WBL chose Royal HaskoningDHV to help implement these technologies. With 140 years of experience, in-depth domain knowledge, and a team of ICT infrastructure experts, the company had everything needed to bring the water authority’s ideas into the real world.

The project began with Royal HaskoningDHV implementing a digital twin for all the water authority's sewage pumping stations. This digital twin consists of a Big Data Platform that centrally stores data from various sources, and machine learning models that provide vital, real-time and predictive information.

The insights from this digital twin are managed in a central control room, that currently oversees 17 STPs, 5 sludge dewatering plants, 149 WBL sewage pumping stations and 800 municipal pumping stations.

“We partnered with Royal HaskoningDHV because they know a lot about the water industry, and about implementing and maintaining complex information systems,” says Léon Verhaegen.

The result

Smarter, more efficient and future-proof

Armed with the insights from the digital twin, the WBL water authority can now detect problems in its pumping stations early enough to take proactive action — and can also clearly see if it’s meeting the purchase obligations of various municipalities.

The digital twin uses machine learning to detect at a very early stage that the functioning of pumps is beginning to deteriorate (e.g. due to wear and tear) or that pressure pipes are in danger of becoming blocked. The digital twin relays these warnings to the central control room, whereupon the operators take action to prevent the impending The digital twin uses machine learning to detect at a very early stage that the functioning of pumps is beginning to deteriorate (e.g. due to wear and tear) or that pressure pipes are in danger of becoming blocked. The digital twin relays these warnings to the central control room, whereupon the operators take action to prevent the impending failures.

“The most important thing we achieved in this project is giving our operators the tools and resources they need to improve performance,” says Verhaegen. “That hugely improves our efficiency and emergency response.”

This preventative approach to pumping station management makes operations and maintenance smarter, more efficient and more future-proof. But the scope of this project doesn’t end here — if ambitions are realised, this work could mean not just big changes for WBL, but the beginning of an industry-wide transformation.

“In the future, we want to extend this solution to 3,000 municipal water stations,” says Verhaegen. “That way we can gain real insight into how the water chain works as a whole.”